Exclude subject 14: squeezed emergency ball while in the scanner. Exclude subject 30: subject wanted to discontinue after round 7 because of discomfort caused by glasses Exclude subject 34: the participant perceived the peripheral nerve stimulation to be uncomfortable, so stopped the scan after Round 2.
## `summarise()` has grouped output by 'sub', 'nquestion', 'round_text'. You can override using the `.groups` argument.
## `summarise()` has grouped output by 'sub'. You can override using the `.groups` argument.
## Joining, by = "sub"
## Joining, by = "sub"
| Prescan Performance | |||
|---|---|---|---|
| nquestion | correct1 | conf | conf_correct2 |
| 06 | |||
| 1 | 0.125 | 0.417 | 0.250 |
| 2 | 0.375 | 0.417 | 0.250 |
| 3 | 0.750 | 0.417 | 0.250 |
| 07 | |||
| 1 | 0.250 | 0.625 | 0.583 |
| 2 | 0.750 | 0.625 | 0.583 |
| 3 | 1.000 | 0.625 | 0.583 |
| 08 | |||
| 1 | 0.500 | 0.667 | 0.667 |
| 2 | 1.000 | 0.667 | 0.667 |
| 3 | 1.000 | 0.667 | 0.667 |
| 09 | |||
| 1 | 0.500 | 0.458 | 0.417 |
| 2 | 0.750 | 0.458 | 0.417 |
| 3 | 0.875 | 0.458 | 0.417 |
| 10 | |||
| 1 | 0.375 | 0.542 | 0.458 |
| 2 | 0.750 | 0.542 | 0.458 |
| 3 | 0.875 | 0.542 | 0.458 |
| 11 | |||
| 1 | 0.625 | 0.583 | 0.583 |
| 2 | 0.875 | 0.583 | 0.583 |
| 3 | 1.000 | 0.583 | 0.583 |
| 12 | |||
| 1 | 0.125 | 0.708 | 0.583 |
| 2 | 0.625 | 0.708 | 0.583 |
| 3 | 1.000 | 0.708 | 0.583 |
| 13 | |||
| 1 | 0.000 | 0.375 | 0.333 |
| 2 | 0.500 | 0.375 | 0.333 |
| 3 | 0.750 | 0.375 | 0.333 |
| 15 | |||
| 1 | 0.250 | 0.500 | 0.500 |
| 2 | 0.625 | 0.500 | 0.500 |
| 3 | 1.000 | 0.500 | 0.500 |
| 16 | |||
| 1 | 0.250 | 0.333 | 0.333 |
| 2 | 0.750 | 0.333 | 0.333 |
| 3 | 0.875 | 0.333 | 0.333 |
| 17 | |||
| 1 | 0.500 | 0.625 | 0.625 |
| 2 | 0.750 | 0.625 | 0.625 |
| 3 | 1.000 | 0.625 | 0.625 |
| 18 | |||
| 1 | 0.500 | 0.625 | 0.625 |
| 2 | 0.875 | 0.625 | 0.625 |
| 3 | 1.000 | 0.625 | 0.625 |
| 19 | |||
| 1 | 0.375 | 0.458 | 0.458 |
| 2 | 0.625 | 0.458 | 0.458 |
| 3 | 1.000 | 0.458 | 0.458 |
| 20 | |||
| 1 | 0.250 | 0.583 | 0.542 |
| 2 | 0.625 | 0.583 | 0.542 |
| 3 | 1.000 | 0.583 | 0.542 |
| 21 | |||
| 1 | 0.500 | 0.500 | 0.500 |
| 2 | 0.875 | 0.500 | 0.500 |
| 3 | 1.000 | 0.500 | 0.500 |
| 22 | |||
| 1 | 0.375 | 0.708 | 0.625 |
| 2 | 0.875 | 0.708 | 0.625 |
| 3 | 1.000 | 0.708 | 0.625 |
| 23 | |||
| 1 | 0.125 | 0.125 | 0.125 |
| 2 | 0.375 | 0.125 | 0.125 |
| 3 | 0.500 | 0.125 | 0.125 |
| 24 | |||
| 1 | 0.375 | 0.292 | 0.250 |
| 2 | 0.750 | 0.292 | 0.250 |
| 3 | 0.625 | 0.292 | 0.250 |
| 25 | |||
| 1 | 0.250 | 0.583 | 0.500 |
| 2 | 0.625 | 0.583 | 0.500 |
| 3 | 1.000 | 0.583 | 0.500 |
| 26 | |||
| 1 | 0.500 | 0.625 | 0.625 |
| 2 | 0.875 | 0.625 | 0.625 |
| 3 | 1.000 | 0.625 | 0.625 |
| 27 | |||
| 1 | 0.625 | 0.583 | 0.542 |
| 2 | 0.750 | 0.583 | 0.542 |
| 3 | 1.000 | 0.583 | 0.542 |
| 28 | |||
| 1 | 0.500 | 0.625 | 0.542 |
| 2 | 0.875 | 0.625 | 0.542 |
| 3 | 0.875 | 0.625 | 0.542 |
| 29 | |||
| 1 | 0.750 | 0.458 | 0.458 |
| 2 | 0.625 | 0.458 | 0.458 |
| 3 | 1.000 | 0.458 | 0.458 |
| 31 | |||
| 1 | 0.625 | 0.292 | 0.167 |
| 2 | 0.375 | 0.292 | 0.167 |
| 3 | 0.625 | 0.292 | 0.167 |
| 32 | |||
| 1 | 0.625 | 0.750 | 0.708 |
| 2 | 1.000 | 0.750 | 0.708 |
| 3 | 1.000 | 0.750 | 0.708 |
| 33 | |||
| 1 | 0.125 | 0.458 | 0.417 |
| 2 | 0.625 | 0.458 | 0.417 |
| 3 | 0.875 | 0.458 | 0.417 |
| 35 | |||
| 1 | 0.375 | 0.375 | 0.250 |
| 2 | 0.375 | 0.375 | 0.250 |
| 3 | 0.875 | 0.375 | 0.250 |
| 36 | |||
| 1 | 0.375 | 0.500 | 0.417 |
| 2 | 0.875 | 0.500 | 0.417 |
| 3 | 0.875 | 0.500 | 0.417 |
| 37 | |||
| 1 | 0.375 | 0.667 | 0.583 |
| 2 | 0.625 | 0.667 | 0.583 |
| 3 | 1.000 | 0.667 | 0.583 |
| 38 | |||
| 1 | 0.500 | 0.708 | 0.667 |
| 2 | 1.000 | 0.708 | 0.667 |
| 3 | 1.000 | 0.708 | 0.667 |
|
1
accuracy < 0.5 are highlighted
2
confidence correct < 0.25 are highlighted
|
|||
| Scan Performance | |||
|---|---|---|---|
| sub | correct1 | conf | conf_correct2 |
| 06 | 0.950 | 0.750 | 0.750 |
| 07 | 0.950 | 0.825 | 0.800 |
| 08 | 0.925 | 0.900 | 0.900 |
| 09 | 0.800 | 0.375 | 0.375 |
| 10 | 0.725 | 0.475 | 0.475 |
| 11 | 0.925 | 0.900 | 0.875 |
| 12 | 0.725 | 0.750 | 0.725 |
| 13 | 0.350 | 0.275 | 0.250 |
| 15 | 0.975 | 0.800 | 0.775 |
| 16 | 0.825 | 0.600 | 0.575 |
| 17 | 0.950 | 0.950 | 0.950 |
| 18 | 0.975 | 0.950 | 0.925 |
| 19 | 0.800 | 0.600 | 0.575 |
| 20 | 0.750 | 0.700 | 0.650 |
| 21 | 0.675 | 0.200 | 0.200 |
| 22 | 0.800 | 0.850 | 0.750 |
| 23 | 0.750 | 0.350 | 0.350 |
| 24 | 0.650 | 0.300 | 0.300 |
| 25 | 0.850 | 0.825 | 0.750 |
| 26 | 0.875 | 0.800 | 0.775 |
| 27 | 0.700 | 0.325 | 0.325 |
| 28 | 0.950 | 0.925 | 0.925 |
| 29 | 0.850 | 0.575 | 0.575 |
| 31 | 0.575 | 0.700 | 0.500 |
| 32 | 0.925 | 0.875 | 0.875 |
| 33 | 0.725 | 0.425 | 0.400 |
| 35 | 0.850 | 0.600 | 0.550 |
| 36 | 0.650 | 0.475 | 0.475 |
| 37 | 0.900 | 0.825 | 0.800 |
| 38 | 0.950 | 0.825 | 0.825 |
|
1
accuracy < 0.5 are highlighted
2
confidence correct < 0.25 are highlighted
|
|||
## `summarise()` has grouped output by 'sub'. You can override using the `.groups` argument.
| Postscan1 Performance | ||
|---|---|---|
| confidence range from 0 - 2. | ||
| npic | m1 | conf |
| 06 | ||
| 30 | 0.875 | 1.500 |
| 45 | 1.000 | 1.875 |
| 60 | 1.000 | 1.875 |
| 75 | 1.000 | 2.000 |
| 07 | ||
| 30 | 0.500 | 1.250 |
| 45 | 0.875 | 1.750 |
| 60 | 1.000 | 2.000 |
| 75 | 1.000 | 2.000 |
| 08 | ||
| 30 | 0.500 | 1.125 |
| 45 | 1.000 | 2.000 |
| 60 | 1.000 | 2.000 |
| 75 | 1.000 | 2.000 |
| 09 | ||
| 30 | 0.000 | 0.250 |
| 45 | 0.500 | 0.875 |
| 60 | 1.000 | 1.750 |
| 75 | 1.000 | 2.000 |
| 10 | ||
| 30 | 0.500 | 0.625 |
| 45 | 0.500 | 1.000 |
| 60 | 0.625 | 1.125 |
| 75 | 1.000 | 2.000 |
| 11 | ||
| 30 | 1.000 | 1.750 |
| 45 | 1.000 | 2.000 |
| 60 | 1.000 | 2.000 |
| 75 | 1.000 | 2.000 |
| 12 | ||
| 30 | 0.500 | 1.250 |
| 45 | 0.875 | 1.625 |
| 60 | 0.875 | 1.750 |
| 75 | 1.000 | 1.750 |
| 13 | ||
| 30 | 0.000 | 0.000 |
| 45 | 0.375 | 0.500 |
| 60 | 0.500 | 1.250 |
| 75 | 0.750 | 1.625 |
| 15 | ||
| 30 | 0.625 | 1.250 |
| 45 | 1.000 | 2.000 |
| 60 | 1.000 | 2.000 |
| 75 | 1.000 | 2.000 |
| 16 | ||
| 30 | 0.625 | 0.750 |
| 45 | 1.000 | 1.875 |
| 60 | 1.000 | 2.000 |
| 75 | 1.000 | 2.000 |
| 17 | ||
| 30 | 1.000 | 2.000 |
| 45 | 1.000 | 2.000 |
| 60 | 1.000 | 2.000 |
| 75 | 1.000 | 2.000 |
| 18 | ||
| 30 | 0.500 | 0.750 |
| 45 | 1.000 | 2.000 |
| 60 | 1.000 | 2.000 |
| 75 | 1.000 | 2.000 |
| 19 | ||
| 30 | 0.250 | 1.000 |
| 45 | 1.000 | 2.000 |
| 60 | 1.000 | 2.000 |
| 75 | 1.000 | 2.000 |
| 20 | ||
| 30 | 0.500 | 2.000 |
| 45 | 0.875 | 2.000 |
| 60 | 0.875 | 2.000 |
| 75 | 0.875 | 2.000 |
| 21 | ||
| 30 | 0.000 | 0.000 |
| 45 | 0.500 | 1.000 |
| 60 | 1.000 | 1.500 |
| 75 | 1.000 | 2.000 |
| 22 | ||
| 30 | 0.625 | 1.125 |
| 45 | 1.000 | 2.000 |
| 60 | 0.875 | 1.750 |
| 75 | 1.000 | 2.000 |
| 23 | ||
| 30 | 0.125 | 0.125 |
| 45 | 0.250 | 0.250 |
| 60 | 0.375 | 0.500 |
| 75 | 0.500 | 0.875 |
| 24 | ||
| 30 | 0.125 | 0.625 |
| 45 | 0.500 | 1.125 |
| 60 | 0.750 | 1.625 |
| 75 | 0.875 | 2.000 |
| 25 | ||
| 30 | 0.500 | 1.000 |
| 45 | 1.000 | 2.000 |
| 60 | 1.000 | 2.000 |
| 75 | 1.000 | 2.000 |
| 26 | ||
| 30 | 0.500 | 0.875 |
| 45 | 1.000 | 2.000 |
| 60 | 1.000 | 2.000 |
| 75 | 1.000 | 2.000 |
| 27 | ||
| 30 | 0.125 | 0.125 |
| 45 | 0.875 | 1.375 |
| 60 | 0.875 | 1.375 |
| 75 | 0.875 | 1.375 |
| 28 | ||
| 30 | 0.625 | 1.125 |
| 45 | 1.000 | 2.000 |
| 60 | 1.000 | 2.000 |
| 75 | 1.000 | 2.000 |
| 29 | ||
| 30 | 0.125 | 0.250 |
| 45 | 0.625 | 1.375 |
| 60 | 1.000 | 2.000 |
| 75 | 1.000 | 2.000 |
| 31 | ||
| 30 | 0.125 | 0.125 |
| 45 | 0.500 | 1.125 |
| 60 | 1.000 | 1.750 |
| 75 | 1.000 | 2.000 |
| 32 | ||
| 30 | 0.875 | 1.625 |
| 45 | 1.000 | 2.000 |
| 60 | 1.000 | 2.000 |
| 75 | 1.000 | 2.000 |
| 33 | ||
| 30 | 0.000 | 0.000 |
| 45 | 0.500 | 1.000 |
| 60 | 1.000 | 1.625 |
| 75 | 1.000 | 2.000 |
| 35 | ||
| 30 | 0.875 | 1.750 |
| 45 | 1.000 | 2.000 |
| 60 | 1.000 | 2.000 |
| 75 | 1.000 | 2.000 |
| 36 | ||
| 30 | 0.875 | 1.375 |
| 45 | 1.000 | 2.000 |
| 60 | 1.000 | 2.000 |
| 75 | 1.000 | 2.000 |
| 37 | ||
| 30 | 0.500 | 1.000 |
| 45 | 1.000 | 2.000 |
| 60 | 1.000 | 2.000 |
| 75 | 1.000 | 2.000 |
| 38 | ||
| 30 | 0.500 | 1.250 |
| 45 | 1.000 | 2.000 |
| 60 | 1.000 | 2.000 |
| 75 | 1.000 | 2.000 |
|
1
accuracy <= 0.5 are highlighted
|
||
| Post scan 2 mean accuracy per subject | ||
|---|---|---|
| sub | m1 | m_npic2 |
| 06 | 0.7500 | 32.40000 |
| 07 | 1.0000 | 31.81250 |
| 08 | 0.8750 | 32.37500 |
| 09 | 1.0000 | 51.31250 |
| 10 | 1.0000 | 49.06250 |
| 11 | 1.0000 | 29.12500 |
| 12 | 0.8125 | 34.64286 |
| 13 | 0.8750 | 53.25000 |
| 15 | 1.0000 | 32.25000 |
| 16 | 1.0000 | 42.81250 |
| 17 | 1.0000 | 29.12500 |
| 18 | 0.9375 | 34.31250 |
| 19 | 1.0000 | 42.50000 |
| 20 | 0.7500 | 30.25000 |
| 21 | 1.0000 | 50.62500 |
| 22 | 1.0000 | 34.43750 |
| 23 | 1.0000 | 80.37500 |
| 24 | 1.0000 | 46.31250 |
| 25 | 0.9375 | 35.93750 |
| 26 | 0.9375 | 34.81250 |
| 27 | 1.0000 | 60.12500 |
| 28 | 1.0000 | 34.12500 |
| 29 | 0.9375 | 50.87500 |
| 31 | 1.0000 | 49.93750 |
| 32 | 0.9375 | 34.87500 |
| 33 | 0.7500 | 49.50000 |
| 35 | 1.0000 | 30.06250 |
| 36 | 0.8750 | 33.37500 |
| 37 | 1.0000 | 33.06250 |
| 38 | 0.9375 | 31.06250 |
|
1
accuracy <= 0.5 are highlighted
2
average img index > 75 are highlighted
|
||
Participant were instructed to answer the expected destination for 3 times during the route: once at Same, once at Overlapping, and once at non-overlapping. They also indicated their confidence towards the choice (sure vs. unsure).
## `summarise()` has grouped output by 'nquestion'. You can override using the `.groups` argument.
ANVOA for Accuracy:
## $ANOVA
## Effect DFn DFd F p p<.05 ges
## 1 round 1 29 4.24970692 4.832187e-02 * 0.040855014
## 2 nquestion 2 58 132.36568849 2.415557e-22 * 0.623160490
## 3 round:nquestion 2 58 0.04556167 9.554948e-01 0.000544922
ANVOA for Confidence:
## $ANOVA
## Effect DFn DFd F p p<.05 ges
## 1 round 1 29 0.03519417 8.524958e-01 0.0002862869
## 2 nquestion 2 58 207.25302176 3.815910e-27 * 0.7920871662
## 3 round:nquestion 2 58 2.82269064 6.763507e-02 0.0219857163
ANVOA for high confidence accuracy:
## $ANOVA
## Effect DFn DFd F p p<.05 ges
## 1 round 1 29 0.8854962 3.544738e-01 0.004939796
## 2 nquestion 2 58 208.6023535 3.234972e-27 * 0.819878727
## 3 round:nquestion 2 58 6.6178923 2.577292e-03 * 0.044612420
t-test for mean:
##
## Paired t-test
##
## data: sub_plot %>% filter(round == 1 & nquestion == 1) %>% .$m and sub_plot %>% filter(round == 2 & nquestion == 1) %>% .$m
## t = -1.2732, df = 29, p-value = 0.213
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.19547475 0.04547475
## sample estimates:
## mean of the differences
## -0.075
##
## Paired t-test
##
## data: sub_plot %>% filter(round == 1 & nquestion == 2) %>% .$m and sub_plot %>% filter(round == 2 & nquestion == 2) %>% .$m
## t = -1.0701, df = 29, p-value = 0.2934
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.1698264 0.0531597
## sample estimates:
## mean of the differences
## -0.05833333
##
## Paired t-test
##
## data: sub_plot %>% filter(round == 1 & nquestion == 3) %>% .$m and sub_plot %>% filter(round == 2 & nquestion == 3) %>% .$m
## t = -2.34, df = 29, p-value = 0.02638
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.140552684 -0.009447316
## sample estimates:
## mean of the differences
## -0.075
t-test for confidence:
##
## Paired t-test
##
## data: sub_plot %>% filter(round == 1 & nquestion == 1) %>% .$conf and sub_plot %>% filter(round == 2 & nquestion == 1) %>% .$conf
## t = 0.72449, df = 29, p-value = 0.4746
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.06076631 0.12743297
## sample estimates:
## mean of the differences
## 0.03333333
##
## Paired t-test
##
## data: sub_plot %>% filter(round == 1 & nquestion == 2) %>% .$conf and sub_plot %>% filter(round == 2 & nquestion == 2) %>% .$conf
## t = 0.59349, df = 29, p-value = 0.5575
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.06115311 0.11115311
## sample estimates:
## mean of the differences
## 0.025
##
## Paired t-test
##
## data: sub_plot %>% filter(round == 1 & nquestion == 3) %>% .$conf and sub_plot %>% filter(round == 2 & nquestion == 3) %>% .$conf
## t = -2.0685, df = 29, p-value = 0.04761
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.149156292 -0.000843708
## sample estimates:
## mean of the differences
## -0.075
t-test for high confidence accuracy:
##
## Paired t-test
##
## data: sub_plot %>% filter(round == 1 & nquestion == 1) %>% .$cor_conf and sub_plot %>% filter(round == 2 & nquestion == 1) %>% .$cor_conf
## t = 2.5357, df = 29, p-value = 0.01687
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.01128351 0.10538315
## sample estimates:
## mean of the differences
## 0.05833333
##
## Paired t-test
##
## data: sub_plot %>% filter(round == 1 & nquestion == 2) %>% .$cor_conf and sub_plot %>% filter(round == 2 & nquestion == 2) %>% .$cor_conf
## t = -0.40251, df = 29, p-value = 0.6903
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.10135398 0.06802064
## sample estimates:
## mean of the differences
## -0.01666667
##
## Paired t-test
##
## data: sub_plot %>% filter(round == 1 & nquestion == 3) %>% .$cor_conf and sub_plot %>% filter(round == 2 & nquestion == 3) %>% .$cor_conf
## t = -2.7651, df = 29, p-value = 0.009792
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.18846254 -0.02820413
## sample estimates:
## mean of the differences
## -0.1083333
Early vs. Late stop accuracy during scan:
Accuracy per round:
## `summarise()` has grouped output by 'round'. You can override using the `.groups` argument.
Distribution of picture index:
Grouped in 10:
## `summarise()` has grouped output by 'npic_10', 'route'. You can override using the `.groups` argument.
Grouped in 5:
## `summarise()` has grouped output by 'npic_5', 'route'. You can override using the `.groups` argument.
Every picture:
## `summarise()` has grouped output by 'sub'. You can override using the `.groups` argument.
Average accuracy = 0.94375
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.